Empowering individuals to make good financial decisions
Introduction to Salary Recommender
In this case study, we will understand the pain problems of the working professionals in terms of budgeting the salary and also understand how we can solve these problems using salary recommender tool
What was done as part of this Salary Recommender case study
Step 1: Pain Problem
Compiled issues from use of financial apps/manual calculators such as manual entry, rigid templates, and lack of personalized recommendations.
Step 2: User Research
Formulated hypotheses, identified user cohorts, floated surveys, and analyzed the results to validate pain points and needs.
Step 3: Analysis
Synthesized survey data into actionable insights — preferences for personalized, simple, and goal-driven budgeting tools emerged.
Step 4: Product Strategy
Vision: Empower individuals to make financial decisions easily.
Target: 22–35 yr working professionals.
Goals: Personalized recommendations, mobile-first, social-friendly.
Step 5: Development
Built lightweight web app using HTML, JS.
Modular design for scalability.
Deployed on WordPress as embedded tool for finance blog.
Step 6: Go-to Market
Used blog, LinkedIn, Twitter & YouTube for traffic.
Ran SEO + blog analytics for acquisition.
Positioned tool through storytelling-based case study.
Pain Problems
User Research
Hypothesis
Most of the users who does budgeting are doing it manually in excel
Users Targeted
Users from IT and MBA background
Methods
- Surveys (28 responses)
- 1:1 Interviews
Key Insights
- 42% of users are doing manual tracking of budgeting in excel
- 71% of users needed clear breakdowns of salary budgeting
Product Strategy
️Vision
Empowering individuals to make financial decisions easily
Target Audience
Age group: 22 – 60, working professionals
Goals
- Personalized Recommendation
- Mobile Friendly
- Knowledge Sharing
- Monetization & Scalability
Key Metrics
- Website traffic increment of 5% quarterly
- Engagement of 40% users
Road Map
- MVP – Plain salary budget recommendation
- Release 2.0 (Month 2) – Customizable additions under each bucket of needs, wants and savings
- Release 3.0 (Month 3) – Saving recommendation, basically where to park your savings
Solution
- Use AI tools to develop the tool using PHP, HTML and JavaScript
- Tweak it for the WordPress website
Go-To-Market Strategy
- Leverage social media platforms like LinkedIn, Twitter, WhatsApp and YouTube to promote the tool
- Use SEO optimization techniques
- Use Analytics tools to gather user engagement, acquisition insights
- Publish case study on the tool in the personal blog
Now that we have seen the case study, please check out the salary recommender tool here – Salary Recommender Tool